Screening for malnutrition with malnutrition inflammation score and geriatric nutritional risk index in hemodialysis patients
Bibliographic record
Abstract
BACKGROUND AND AIMS: Screening malnutrition, which is the most common complication in hemodialysis patients, is extremely important for these patients. Malnutrition inflammation score (MIS) and geriatric nutritional risk index (GNRI) are malnutrition screening tests used in hemodialysis patients in recent years. The purposes of this study are to evaluate the nutritional status of hemodialysis patients with different screening tests and to determine the cutoff values for this disease-specific MIS and GNRI. METHODS: The study was conducted with 194 adult patients including 98 males and 96 females whose mean age was 53.1 ± 10.96. Subjective global assessment (SGA) and MIS tests were applied, and the GNRI value was calculated for screening malnutrition. MIS and GNRI cutoff values were obtained by adopting the SGA scores as a standard and drawing a receiver operating characteristic curve. The tatistical Package for the Social Sciences-22.0 package program was used in the analysis. RESULTS: According to SGA, 70.7% of the patients were nourished, 21.1% were mildly-moderately malnourished, and 8.2% were found to be severely malnourished. The optimal cutoff value predicted for malnutrition was 6.5 points (94.7% sensitivity and 98.5% specificity) for MIS and 86.0 points (64.9% sensitivity and 62.8% specificity) for GNRI. Based on these cutoff values, 28.9% of the patients were determined to be malnourished according to MIS and 45.4% according to GNRI. CONCLUSION: In conclusion, screening tests are very important in the early identification of malnutrition in hemodialysis patients. This study was conducted to evaluate the malnutrition of hemodialysis patients with different screening tests. At the end of the study, the availability of MIS was found to be high in detecting malnutrition in hemodialysis patients because of its high accuracy and sensitivity of MIS. The cutoff points we identified for both MIS and GNRI are thought to facilitate the determination of malnutrition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".